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majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-Q5_K_M

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Model Card

[!TIP] KV-cache quantization without any fork (recommended, 2026): upstream llama.cpp/Ollama now cover this natively — use -ctk q8_0 -ctv q8_0 (~half KV memory, negligible quality loss: perplexity +0.002–0.05) or -ctk q4_0 -ctv q4_0 (~quarter memory, ≈7.6% perplexity increase). In Ollama: OLLAMA_KV_CACHE_TYPE=q8_0 with OLLAMA_FLASH_ATTENTION=1. Keep K and V types symmetric to stay on the fast fused Flash-Attention path. Since April 2026, mainline llama.cpp also applies Hadamard rotation to KV activations (PR #21038), which greatly improves low-bit KV quality (opt-out: LLAMA_ATTN_ROT_DISABLE=1). The RotorQuant/TurboQuant fork flow below is experimental/legacy: the TurboQuant llama.cpp PR was closed without merging (June 2026) and the fork is unmaintained relative to mainline. It is NOT required to use this model.

Nemotron-3-Nano-Omni-30B-A3B-Reasoning - TurboQuant GGUF Q5KM

GGUF Q5KM quantization of Nemotron-3-Nano-Omni-30B-A3B-Reasoning (nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16) with TurboQuant weight method.

The Q5_K_M.gguf binary in this repo is loaded by llama.cpp / llama-mtmd-cli. For multimodal inference (text + image + audio + video) pair this with the multimodal projector: `majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-mmproj-F16`.

For the matched-KV stack — TurboQuant weights + TurboQuant KV-cache modifier — For the runtime KV-cache modifier itself (weight-agnostic), see majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant.

Quickstart

bash

# 1. Download the GGUF + the multimodal projector
huggingface-cli download majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-TurboQuant-GGUF-Q5_K_M Q5_K_M.gguf --local-dir ./model
huggingface-cli download majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-mmproj-F16 mmproj-F16.gguf --local-dir ./mmproj

# 2. Multimodal inference (text + image + audio + video)
llama-mtmd-cli \
  -m ./model/Q5_K_M.gguf \
  --mmproj ./mmproj/mmproj-F16.gguf \
  --image cat.jpg \
  -p "Describe this image in detail" \
  --temp 0.6 --top-p 0.95 -n 512

# 3. Text-only inference (no mmproj needed)
llama-completion -no-cnv \
  -m ./model/Q5_K_M.gguf \
  -p "What is the capital of France?" \
  --temp 0.6 --top-p 0.95 -n 256

# Disable extended reasoning (default is on):
#   add `--chat-template-kwargs '{"enable_thinking": false}'`

⚠️ Do NOT use llama.cpp built against CUDA 13.2 — produces gibberish. Pin CUDA 12.x or use Metal/CPU.

Modality matrix

Modality	Encoder	Quantization in this variant
Text	LLM backbone (Mamba-2 + Transformer hybrid Sparse MoE)	per the variant suffix
Image	CRADIO v4-H	BF16 (kept full-precision in every non-GGUF variant; GGUF uses mmproj-F16 split file)
Audio	Parakeet-TDT-0.6B-v2	BF16 (same rationale)
Video	Parakeet-TDT-0.6B-v2 + frame sampler	BF16 (≤ 2 min, 256 frames @ 2 FPS)

NVIDIA's official FP8 / NVFP4 recipe keeps both encoders + the cross-modal MLP projectors in BF16 to preserve multimodal accuracy. We follow that convention in every quantized variant we ship.

Runtime quirks

llama.cpp

Use llama-mtmd-cli for multimodal inference; pass --mmproj mmproj-F16.gguf (see majentik/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-mmproj-F16).

Do NOT use CUDA 13.2 — produces gibberish. Pin CUDA 12.x or use the Metal/CPU paths.

Ollama

Text-only; multimodal is blocked because Ollama doesn't yet support the mmproj split-file pattern.

Reasoning mode

enable_thinking defaults to True. To disable extended reasoning (e.g., for latency-sensitive cases), pass enable_thinking=False to the chat template / generate call. No separate "no-think" variant card exists — this is a runtime flag, not a model variant.

Quant trade-off (GGUF lane)

Quant	Approx size	Use case	Recommendation
Q2_K	~17 GB	Lossy, low-RAM CPU/edge	Resource-constrained inference
Q3KM	~19 GB	Smaller-than-Q4, modest quality drop	Edge devices with ~16 GB RAM
IQ4_XS	~16 GB	Importance-quant 4-bit, smaller than Q4KM	Best size/quality at 4-bit
Q4KM	~23 GB	Balanced default	Recommended for most users
Q5_K_M	~24 GB	Higher fidelity than Q4	Quality-sensitive applications
Q6_K	~28 GB	Approaching FP16 quality	High-fidelity CPU/edge
Q8_0	~32 GB	Near-lossless reference	Fidelity-critical work
MXFP4_MOE	~17 GB	Microscaling FP4 (MoE-aware)	vLLM / transformers users

(Current variant — Q5_K_M — is bolded.)

Variants in this family

(Showing 56 sibling variants under majentik/nemotron3-nano-omni-30b-*. The current variant — TurboQuant-GGUF-Q5_K_M — is bolded.)

Variant	Runtime	Approx size	Use case
TurboQuant-GGUF-Q5_K_M	llama.cpp	~40 GB	Higher fidelity, more RAM

About the RotorQuant / TurboQuant labels

RotorQuant and TurboQuant are this project's release labels, not distinct quantization algorithms — for any given tier, both brand repos carry byte-identical weights produced with the standard MLX / llama.cpp quantizers. No brand-specific speedup is claimed or measured.